KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao
Abstract
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions, and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.
BibTeX
@inproceedings{zuo-etal-2020-knowdis,
title = "{K}now{D}is: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision",
author = "Zuo, Xinyu and
Chen, Yubo and
Liu, Kang and
Zhao, Jun",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.135/",
doi = "10.18653/v1/2020.coling-main.135",
pages = "1544--1550"
}